Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations15,972
Missing cells0
Missing cells (%)0.0%
Duplicate rows395
Duplicate rows (%)2.5%
Total size in memory6.3 MiB
Average record size in memory412.2 B

Variable types

DateTime1
Numeric18
Categorical5

Alerts

Dataset has 395 (2.5%) duplicate rowsDuplicates
achievement is highly overall correlated with work_life_balance_scoreHigh correlation
core_circle is highly overall correlated with work_life_balance_scoreHigh correlation
places_visited is highly overall correlated with work_life_balance_scoreHigh correlation
supporting_others is highly overall correlated with work_life_balance_scoreHigh correlation
time_for_passion is highly overall correlated with work_life_balance_scoreHigh correlation
todo_completed is highly overall correlated with work_life_balance_scoreHigh correlation
work_life_balance_score is highly overall correlated with achievement and 5 other fieldsHigh correlation
fruits_veggies has 552 (3.5%) zerosZeros
places_visited has 1016 (6.4%) zerosZeros
core_circle has 312 (2.0%) zerosZeros
supporting_others has 740 (4.6%) zerosZeros
achievement has 1302 (8.2%) zerosZeros
donation has 2500 (15.7%) zerosZeros
todo_completed has 540 (3.4%) zerosZeros
flow has 1330 (8.3%) zerosZeros
live_vision has 2518 (15.8%) zerosZeros
lost_vacation has 8115 (50.8%) zerosZeros
daily_shouting has 2430 (15.2%) zerosZeros
personal_awards has 545 (3.4%) zerosZeros
time_for_passion has 1797 (11.3%) zerosZeros
weekly_meditation has 297 (1.9%) zerosZeros

Reproduction

Analysis started2025-10-26 10:15:59.545662
Analysis finished2025-10-26 10:16:50.706290
Duration51.16 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct7002
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Memory size124.9 KiB
Minimum2015-07-07 00:00:00
Maximum2021-03-14 09:03:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-26T11:16:50.851443image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:51.011275image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fruits_veggies
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9226772
Minimum0
Maximum5
Zeros552
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:51.134564image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4426942
Coefficient of variation (CV)0.49362079
Kurtosis-1.0070367
Mean2.9226772
Median Absolute Deviation (MAD)1
Skewness-0.050981609
Sum46681
Variance2.0813666
MonotonicityNot monotonic
2025-10-26T11:16:51.402755image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
33737
23.4%
23570
22.4%
53141
19.7%
42551
16.0%
12421
15.2%
0552
 
3.5%
ValueCountFrequency (%)
0552
 
3.5%
12421
15.2%
23570
22.4%
33737
23.4%
42551
16.0%
53141
19.7%
ValueCountFrequency (%)
53141
19.7%
42551
16.0%
33737
23.4%
23570
22.4%
12421
15.2%
0552
 
3.5%

daily_stress
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size904.8 KiB
3
4398 
2
3407 
4
2960 
1
2478 
5
2052 
Other values (2)
677 

Length

Max length6
Median length1
Mean length1.000313
Min length1

Characters and Unicode

Total characters15,977
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
34398
27.5%
23407
21.3%
42960
18.5%
12478
15.5%
52052
12.8%
0676
 
4.2%
1/1/001
 
< 0.1%

Length

2025-10-26T11:16:51.482729image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T11:16:51.605185image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
34398
27.5%
23407
21.3%
42960
18.5%
12478
15.5%
52052
12.8%
0676
 
4.2%
1/1/001
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
34398
27.5%
23407
21.3%
42960
18.5%
12480
15.5%
52052
12.8%
0678
 
4.2%
/2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
34398
27.5%
23407
21.3%
42960
18.5%
12480
15.5%
52052
12.8%
0678
 
4.2%
/2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
34398
27.5%
23407
21.3%
42960
18.5%
12480
15.5%
52052
12.8%
0678
 
4.2%
/2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
34398
27.5%
23407
21.3%
42960
18.5%
12480
15.5%
52052
12.8%
0678
 
4.2%
/2
 
< 0.1%

places_visited
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2329702
Minimum0
Maximum10
Zeros1016
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:51.693519image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3119123
Coefficient of variation (CV)0.6328934
Kurtosis-1.2520628
Mean5.2329702
Median Absolute Deviation (MAD)3
Skewness0.17049443
Sum83581
Variance10.968763
MonotonicityNot monotonic
2025-10-26T11:16:51.786709image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
103558
22.3%
51862
11.7%
31840
11.5%
21787
11.2%
41503
9.4%
11253
 
7.8%
61136
 
7.1%
01016
 
6.4%
8881
 
5.5%
7878
 
5.5%
ValueCountFrequency (%)
01016
6.4%
11253
7.8%
21787
11.2%
31840
11.5%
41503
9.4%
51862
11.7%
61136
7.1%
7878
5.5%
8881
5.5%
9258
 
1.6%
ValueCountFrequency (%)
103558
22.3%
9258
 
1.6%
8881
 
5.5%
7878
 
5.5%
61136
 
7.1%
51862
11.7%
41503
9.4%
31840
11.5%
21787
11.2%
11253
 
7.8%

core_circle
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5080766
Minimum0
Maximum10
Zeros312
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:51.882337image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8403336
Coefficient of variation (CV)0.51566704
Kurtosis-0.96679654
Mean5.5080766
Median Absolute Deviation (MAD)2
Skewness0.20243054
Sum87975
Variance8.0674947
MonotonicityNot monotonic
2025-10-26T11:16:51.993871image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
102784
17.4%
52410
15.1%
42151
13.5%
31941
12.2%
61702
10.7%
21354
8.5%
71141
7.1%
81090
 
6.8%
1719
 
4.5%
9368
 
2.3%
ValueCountFrequency (%)
0312
 
2.0%
1719
 
4.5%
21354
8.5%
31941
12.2%
42151
13.5%
52410
15.1%
61702
10.7%
71141
7.1%
81090
6.8%
9368
 
2.3%
ValueCountFrequency (%)
102784
17.4%
9368
 
2.3%
81090
 
6.8%
71141
7.1%
61702
10.7%
52410
15.1%
42151
13.5%
31941
12.2%
21354
8.5%
1719
 
4.5%

supporting_others
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6164538
Minimum0
Maximum10
Zeros740
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:52.094491image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.242021
Coefficient of variation (CV)0.5772363
Kurtosis-1.2705833
Mean5.6164538
Median Absolute Deviation (MAD)3
Skewness0.055665314
Sum89706
Variance10.5107
MonotonicityNot monotonic
2025-10-26T11:16:52.233179image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
103994
25.0%
51915
12.0%
31835
11.5%
41646
10.3%
21576
 
9.9%
61119
 
7.0%
71053
 
6.6%
8917
 
5.7%
1883
 
5.5%
0740
 
4.6%
ValueCountFrequency (%)
0740
 
4.6%
1883
5.5%
21576
9.9%
31835
11.5%
41646
10.3%
51915
12.0%
61119
7.0%
71053
6.6%
8917
5.7%
9294
 
1.8%
ValueCountFrequency (%)
103994
25.0%
9294
 
1.8%
8917
 
5.7%
71053
 
6.6%
61119
 
7.0%
51915
12.0%
41646
10.3%
31835
11.5%
21576
 
9.9%
1883
 
5.5%

social_network
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4742675
Minimum0
Maximum10
Zeros116
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:52.337683image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.0866722
Coefficient of variation (CV)0.47676007
Kurtosis-1.3704021
Mean6.4742675
Median Absolute Deviation (MAD)3
Skewness-0.17518471
Sum103407
Variance9.5275452
MonotonicityNot monotonic
2025-10-26T11:16:52.436396image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
105456
34.2%
51912
 
12.0%
31593
 
10.0%
41560
 
9.8%
21241
 
7.8%
61219
 
7.6%
81011
 
6.3%
7972
 
6.1%
1556
 
3.5%
9336
 
2.1%
ValueCountFrequency (%)
0116
 
0.7%
1556
 
3.5%
21241
7.8%
31593
10.0%
41560
9.8%
51912
12.0%
61219
7.6%
7972
6.1%
81011
6.3%
9336
 
2.1%
ValueCountFrequency (%)
105456
34.2%
9336
 
2.1%
81011
 
6.3%
7972
 
6.1%
61219
 
7.6%
51912
 
12.0%
41560
 
9.8%
31593
 
10.0%
21241
 
7.8%
1556
 
3.5%

achievement
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0007513
Minimum0
Maximum10
Zeros1302
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:52.520694image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7558374
Coefficient of variation (CV)0.68882997
Kurtosis-0.35501519
Mean4.0007513
Median Absolute Deviation (MAD)2
Skewness0.62888421
Sum63900
Variance7.5946397
MonotonicityNot monotonic
2025-10-26T11:16:52.601713image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
22606
16.3%
32538
15.9%
52031
12.7%
41838
11.5%
11595
10.0%
01302
8.2%
101170
7.3%
61145
7.2%
7814
 
5.1%
8693
 
4.3%
ValueCountFrequency (%)
01302
8.2%
11595
10.0%
22606
16.3%
32538
15.9%
41838
11.5%
52031
12.7%
61145
7.2%
7814
 
5.1%
8693
 
4.3%
9240
 
1.5%
ValueCountFrequency (%)
101170
7.3%
9240
 
1.5%
8693
 
4.3%
7814
 
5.1%
61145
7.2%
52031
12.7%
41838
11.5%
32538
15.9%
22606
16.3%
11595
10.0%

donation
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7153143
Minimum0
Maximum5
Zeros2500
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:52.672856image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.8515864
Coefficient of variation (CV)0.68190501
Kurtosis-1.439477
Mean2.7153143
Median Absolute Deviation (MAD)2
Skewness-0.047913413
Sum43369
Variance3.4283723
MonotonicityNot monotonic
2025-10-26T11:16:52.752267image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
54761
29.8%
12668
16.7%
22533
15.9%
02500
15.7%
32210
13.8%
41300
 
8.1%
ValueCountFrequency (%)
02500
15.7%
12668
16.7%
22533
15.9%
32210
13.8%
41300
 
8.1%
54761
29.8%
ValueCountFrequency (%)
54761
29.8%
41300
 
8.1%
32210
13.8%
22533
15.9%
12668
16.7%
02500
15.7%

bmi_range
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size904.8 KiB
1
9413 
2
6559 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15,972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
19413
58.9%
26559
41.1%

Length

2025-10-26T11:16:52.834491image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T11:16:52.903209image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
19413
58.9%
26559
41.1%

Most occurring characters

ValueCountFrequency (%)
19413
58.9%
26559
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19413
58.9%
26559
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19413
58.9%
26559
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19413
58.9%
26559
41.1%

todo_completed
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.745993
Minimum0
Maximum10
Zeros540
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:52.966022image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6240973
Coefficient of variation (CV)0.45668299
Kurtosis-0.70453571
Mean5.745993
Median Absolute Deviation (MAD)2
Skewness-0.35608113
Sum91775
Variance6.8858865
MonotonicityNot monotonic
2025-10-26T11:16:53.048081image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
82587
16.2%
72553
16.0%
52092
13.1%
61666
10.4%
31414
8.9%
41326
8.3%
101083
6.8%
91079
6.8%
21033
 
6.5%
1599
 
3.8%
ValueCountFrequency (%)
0540
 
3.4%
1599
 
3.8%
21033
 
6.5%
31414
8.9%
41326
8.3%
52092
13.1%
61666
10.4%
72553
16.0%
82587
16.2%
91079
6.8%
ValueCountFrequency (%)
101083
6.8%
91079
6.8%
82587
16.2%
72553
16.0%
61666
10.4%
52092
13.1%
41326
8.3%
31414
8.9%
21033
 
6.5%
1599
 
3.8%

flow
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1947784
Minimum0
Maximum10
Zeros1330
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:53.127694image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3575175
Coefficient of variation (CV)0.73792835
Kurtosis0.26619425
Mean3.1947784
Median Absolute Deviation (MAD)2
Skewness0.87012694
Sum51027
Variance5.557889
MonotonicityNot monotonic
2025-10-26T11:16:53.232929image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
23202
20.0%
12983
18.7%
32485
15.6%
41833
11.5%
51483
9.3%
01330
8.3%
61028
 
6.4%
7584
 
3.7%
8552
 
3.5%
10338
 
2.1%
ValueCountFrequency (%)
01330
8.3%
12983
18.7%
23202
20.0%
32485
15.6%
41833
11.5%
51483
9.3%
61028
 
6.4%
7584
 
3.7%
8552
 
3.5%
9154
 
1.0%
ValueCountFrequency (%)
10338
 
2.1%
9154
 
1.0%
8552
 
3.5%
7584
 
3.7%
61028
 
6.4%
51483
9.3%
41833
11.5%
32485
15.6%
23202
20.0%
12983
18.7%

daily_steps
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7036063
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:53.334989image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8910125
Coefficient of variation (CV)0.50687448
Kurtosis-1.1679165
Mean5.7036063
Median Absolute Deviation (MAD)2
Skewness0.039299677
Sum91098
Variance8.3579533
MonotonicityNot monotonic
2025-10-26T11:16:53.434025image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
102700
16.9%
52161
13.5%
61598
10.0%
31567
9.8%
81543
9.7%
21511
9.5%
41504
9.4%
71431
9.0%
11251
7.8%
9706
 
4.4%
ValueCountFrequency (%)
11251
7.8%
21511
9.5%
31567
9.8%
41504
9.4%
52161
13.5%
61598
10.0%
71431
9.0%
81543
9.7%
9706
 
4.4%
102700
16.9%
ValueCountFrequency (%)
102700
16.9%
9706
 
4.4%
81543
9.7%
71431
9.0%
61598
10.0%
52161
13.5%
41504
9.4%
31567
9.8%
21511
9.5%
11251
7.8%

live_vision
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7521287
Minimum0
Maximum10
Zeros2518
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:53.513733image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2309869
Coefficient of variation (CV)0.86110768
Kurtosis-0.56920759
Mean3.7521287
Median Absolute Deviation (MAD)2
Skewness0.74093611
Sum59929
Variance10.439276
MonotonicityNot monotonic
2025-10-26T11:16:53.587546image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
52544
15.9%
02518
15.8%
12485
15.6%
22173
13.6%
102168
13.6%
31768
11.1%
4978
 
6.1%
6461
 
2.9%
7413
 
2.6%
8351
 
2.2%
ValueCountFrequency (%)
02518
15.8%
12485
15.6%
22173
13.6%
31768
11.1%
4978
 
6.1%
52544
15.9%
6461
 
2.9%
7413
 
2.6%
8351
 
2.2%
9113
 
0.7%
ValueCountFrequency (%)
102168
13.6%
9113
 
0.7%
8351
 
2.2%
7413
 
2.6%
6461
 
2.9%
52544
15.9%
4978
 
6.1%
31768
11.1%
22173
13.6%
12485
15.6%

sleep_hours
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0428876
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:53.693506image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median7
Q38
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1990443
Coefficient of variation (CV)0.17024896
Kurtosis1.1728635
Mean7.0428876
Median Absolute Deviation (MAD)1
Skewness-0.3557965
Sum112489
Variance1.4377072
MonotonicityNot monotonic
2025-10-26T11:16:53.765269image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
75566
34.8%
84324
27.1%
63397
21.3%
51025
 
6.4%
9987
 
6.2%
10333
 
2.1%
4252
 
1.6%
349
 
0.3%
221
 
0.1%
118
 
0.1%
ValueCountFrequency (%)
118
 
0.1%
221
 
0.1%
349
 
0.3%
4252
 
1.6%
51025
 
6.4%
63397
21.3%
75566
34.8%
84324
27.1%
9987
 
6.2%
10333
 
2.1%
ValueCountFrequency (%)
10333
 
2.1%
9987
 
6.2%
84324
27.1%
75566
34.8%
63397
21.3%
51025
 
6.4%
4252
 
1.6%
349
 
0.3%
221
 
0.1%
118
 
0.1%

lost_vacation
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8988855
Minimum0
Maximum10
Zeros8115
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:53.830356image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6921795
Coefficient of variation (CV)1.2736548
Kurtosis-0.66794052
Mean2.8988855
Median Absolute Deviation (MAD)0
Skewness0.92051326
Sum46301
Variance13.63219
MonotonicityNot monotonic
2025-10-26T11:16:53.933856image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
08115
50.8%
102239
 
14.0%
51240
 
7.8%
2874
 
5.5%
3807
 
5.1%
4671
 
4.2%
1600
 
3.8%
7587
 
3.7%
8373
 
2.3%
6343
 
2.1%
ValueCountFrequency (%)
08115
50.8%
1600
 
3.8%
2874
 
5.5%
3807
 
5.1%
4671
 
4.2%
51240
 
7.8%
6343
 
2.1%
7587
 
3.7%
8373
 
2.3%
9123
 
0.8%
ValueCountFrequency (%)
102239
14.0%
9123
 
0.8%
8373
 
2.3%
7587
 
3.7%
6343
 
2.1%
51240
7.8%
4671
 
4.2%
3807
 
5.1%
2874
 
5.5%
1600
 
3.8%

daily_shouting
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.930879
Minimum0
Maximum10
Zeros2430
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:54.015589image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6763011
Coefficient of variation (CV)0.91313941
Kurtosis0.53542702
Mean2.930879
Median Absolute Deviation (MAD)1
Skewness1.1180237
Sum46812
Variance7.1625878
MonotonicityNot monotonic
2025-10-26T11:16:54.103024image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
13727
23.3%
22685
16.8%
02430
15.2%
32101
13.2%
41255
 
7.9%
51252
 
7.8%
10785
 
4.9%
7669
 
4.2%
6545
 
3.4%
8378
 
2.4%
ValueCountFrequency (%)
02430
15.2%
13727
23.3%
22685
16.8%
32101
13.2%
41255
 
7.9%
51252
 
7.8%
6545
 
3.4%
7669
 
4.2%
8378
 
2.4%
9145
 
0.9%
ValueCountFrequency (%)
10785
 
4.9%
9145
 
0.9%
8378
 
2.4%
7669
 
4.2%
6545
 
3.4%
51252
 
7.8%
41255
 
7.9%
32101
13.2%
22685
16.8%
13727
23.3%

sufficient_income
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size904.8 KiB
2
11643 
1
4329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15,972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
211643
72.9%
14329
 
27.1%

Length

2025-10-26T11:16:54.213427image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T11:16:54.308134image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
211643
72.9%
14329
 
27.1%

Most occurring characters

ValueCountFrequency (%)
211643
72.9%
14329
 
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
211643
72.9%
14329
 
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
211643
72.9%
14329
 
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
211643
72.9%
14329
 
27.1%

personal_awards
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7115577
Minimum0
Maximum10
Zeros545
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:54.386917image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.0896296
Coefficient of variation (CV)0.54094342
Kurtosis-1.1773697
Mean5.7115577
Median Absolute Deviation (MAD)2
Skewness0.061244757
Sum91225
Variance9.5458109
MonotonicityNot monotonic
2025-10-26T11:16:54.515715image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
103765
23.6%
52210
13.8%
31881
11.8%
41733
10.9%
21382
 
8.7%
61344
 
8.4%
71118
 
7.0%
8946
 
5.9%
1713
 
4.5%
0545
 
3.4%
ValueCountFrequency (%)
0545
 
3.4%
1713
 
4.5%
21382
8.7%
31881
11.8%
41733
10.9%
52210
13.8%
61344
8.4%
71118
7.0%
8946
5.9%
9335
 
2.1%
ValueCountFrequency (%)
103765
23.6%
9335
 
2.1%
8946
 
5.9%
71118
 
7.0%
61344
 
8.4%
52210
13.8%
41733
10.9%
31881
11.8%
21382
 
8.7%
1713
 
4.5%

time_for_passion
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3265715
Minimum0
Maximum10
Zeros1797
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:54.595574image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7292928
Coefficient of variation (CV)0.82045216
Kurtosis-0.19396818
Mean3.3265715
Median Absolute Deviation (MAD)2
Skewness0.84117194
Sum53132
Variance7.4490391
MonotonicityNot monotonic
2025-10-26T11:16:54.683979image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
13285
20.6%
22781
17.4%
31962
12.3%
01797
11.3%
41504
9.4%
51229
 
7.7%
6998
 
6.2%
8906
 
5.7%
10682
 
4.3%
7635
 
4.0%
ValueCountFrequency (%)
01797
11.3%
13285
20.6%
22781
17.4%
31962
12.3%
41504
9.4%
51229
 
7.7%
6998
 
6.2%
7635
 
4.0%
8906
 
5.7%
9193
 
1.2%
ValueCountFrequency (%)
10682
 
4.3%
9193
 
1.2%
8906
 
5.7%
7635
 
4.0%
6998
 
6.2%
51229
 
7.7%
41504
9.4%
31962
12.3%
22781
17.4%
13285
20.6%

weekly_meditation
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2333459
Minimum0
Maximum10
Zeros297
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:54.769339image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.016571
Coefficient of variation (CV)0.4839409
Kurtosis-1.1501249
Mean6.2333459
Median Absolute Deviation (MAD)3
Skewness-0.19251701
Sum99559
Variance9.0997007
MonotonicityNot monotonic
2025-10-26T11:16:54.837749image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
104285
26.8%
72275
14.2%
51977
12.4%
31487
 
9.3%
41310
 
8.2%
21163
 
7.3%
61043
 
6.5%
8993
 
6.2%
1701
 
4.4%
9441
 
2.8%
ValueCountFrequency (%)
0297
 
1.9%
1701
 
4.4%
21163
7.3%
31487
9.3%
41310
8.2%
51977
12.4%
61043
6.5%
72275
14.2%
8993
6.2%
9441
 
2.8%
ValueCountFrequency (%)
104285
26.8%
9441
 
2.8%
8993
 
6.2%
72275
14.2%
61043
 
6.5%
51977
12.4%
41310
 
8.2%
31487
 
9.3%
21163
 
7.3%
1701
 
4.4%

age
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
21 to 35
6108 
36 to 50
4655 
51 or more
3390 
Less than 20
1819 

Length

Max length12
Median length8
Mean length8.8800401
Min length8

Characters and Unicode

Total characters141,832
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36 to 50
2nd row36 to 50
3rd row36 to 50
4th row51 or more
5th row51 or more

Common Values

ValueCountFrequency (%)
21 to 356108
38.2%
36 to 504655
29.1%
51 or more3390
21.2%
Less than 201819
 
11.4%

Length

2025-10-26T11:16:54.949289image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T11:16:55.020260image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
to10763
22.5%
216108
12.7%
356108
12.7%
364655
9.7%
504655
9.7%
513390
 
7.1%
or3390
 
7.1%
more3390
 
7.1%
less1819
 
3.8%
than1819
 
3.8%

Most occurring characters

ValueCountFrequency (%)
31944
22.5%
o17543
12.4%
514153
10.0%
t12582
 
8.9%
310763
 
7.6%
19498
 
6.7%
27927
 
5.6%
r6780
 
4.8%
06474
 
4.6%
e5209
 
3.7%
Other values (7)18959
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)141832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31944
22.5%
o17543
12.4%
514153
10.0%
t12582
 
8.9%
310763
 
7.6%
19498
 
6.7%
27927
 
5.6%
r6780
 
4.8%
06474
 
4.6%
e5209
 
3.7%
Other values (7)18959
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)141832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31944
22.5%
o17543
12.4%
514153
10.0%
t12582
 
8.9%
310763
 
7.6%
19498
 
6.7%
27927
 
5.6%
r6780
 
4.8%
06474
 
4.6%
e5209
 
3.7%
Other values (7)18959
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)141832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31944
22.5%
o17543
12.4%
514153
10.0%
t12582
 
8.9%
310763
 
7.6%
19498
 
6.7%
27927
 
5.6%
r6780
 
4.8%
06474
 
4.6%
e5209
 
3.7%
Other values (7)18959
13.4%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size970.8 KiB
Female
9858 
Male
6114 

Length

Max length6
Median length6
Mean length5.2344102
Min length4

Characters and Unicode

Total characters83,604
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female9858
61.7%
Male6114
38.3%

Length

2025-10-26T11:16:55.127484image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-26T11:16:55.198813image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
ValueCountFrequency (%)
female9858
61.7%
male6114
38.3%

Most occurring characters

ValueCountFrequency (%)
e25830
30.9%
a15972
19.1%
l15972
19.1%
F9858
 
11.8%
m9858
 
11.8%
M6114
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)83604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e25830
30.9%
a15972
19.1%
l15972
19.1%
F9858
 
11.8%
m9858
 
11.8%
M6114
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)83604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e25830
30.9%
a15972
19.1%
l15972
19.1%
F9858
 
11.8%
m9858
 
11.8%
M6114
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)83604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e25830
30.9%
a15972
19.1%
l15972
19.1%
F9858
 
11.8%
m9858
 
11.8%
M6114
 
7.3%

work_life_balance_score
Real number (ℝ)

High correlation 

Distinct1696
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean666.7515
Minimum480
Maximum820.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size124.9 KiB
2025-10-26T11:16:55.357224image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile592.3
Q1636
median667.7
Q3698.5
95-th percentile739.5
Maximum820.2
Range340.2
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation45.019868
Coefficient of variation (CV)0.06752121
Kurtosis-0.10841801
Mean666.7515
Median Absolute Deviation (MAD)31.2
Skewness-0.10467071
Sum10649355
Variance2026.7885
MonotonicityNot monotonic
2025-10-26T11:16:55.533579image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
641.475
 
0.5%
660.557
 
0.4%
670.737
 
0.2%
696.437
 
0.2%
675.836
 
0.2%
672.636
 
0.2%
666.935
 
0.2%
67235
 
0.2%
658.835
 
0.2%
67635
 
0.2%
Other values (1686)15554
97.4%
ValueCountFrequency (%)
4804
< 0.1%
496.12
< 0.1%
4971
 
< 0.1%
4991
 
< 0.1%
504.21
 
< 0.1%
509.71
 
< 0.1%
5141
 
< 0.1%
518.61
 
< 0.1%
5191
 
< 0.1%
521.51
 
< 0.1%
ValueCountFrequency (%)
820.21
< 0.1%
818.31
< 0.1%
816.41
< 0.1%
814.52
< 0.1%
804.32
< 0.1%
802.61
< 0.1%
801.11
< 0.1%
799.41
< 0.1%
799.21
< 0.1%
7991
< 0.1%

Interactions

2025-10-26T11:16:47.952882image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:05.554214image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
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2025-10-26T11:16:15.399627image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:20.037789image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:24.554079image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:28.627482image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:30.069968image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:31.547611image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:33.006776image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:34.547079image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.962601image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
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2025-10-26T11:16:38.851246image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
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2025-10-26T11:16:29.372821image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:30.935161image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:32.356823image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:33.929887image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.376183image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:36.658662image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:38.069715image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:39.598904image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:41.143373image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:42.868476image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:44.825425image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:46.916323image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:49.401830image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:08.594335image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:13.526212image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:18.200335image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:22.713979image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:28.016909image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:29.455671image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:31.006883image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:32.435752image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:33.999124image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.445249image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:36.732976image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:38.146339image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:39.700107image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:41.192331image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:42.983798image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:44.941573image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:47.043543image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:49.534674image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:08.883917image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:13.767236image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:18.476214image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:22.963173image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:28.098198image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:29.547694image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:31.084309image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:32.508001image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:34.073351image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.508537image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:36.804890image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:38.243308image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:39.775129image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:41.281126image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:43.069530image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:45.036165image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:47.175002image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:49.643099image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:09.193286image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:14.070195image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:18.771520image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:23.201056image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:28.181650image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:29.622891image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:31.157907image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:32.581843image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:34.157559image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.591933image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:36.945836image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:38.318466image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:39.854003image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:41.363084image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:43.159182image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:45.135924image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:47.318469image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:49.783751image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:09.486115image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:14.326506image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:19.018001image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:23.478786image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:28.282894image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:29.718462image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:31.250710image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:32.647069image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:34.225535image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.662182image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:37.035659image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:38.398796image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:39.925287image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:41.431877image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:43.250402image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:45.245179image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:47.442718image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:49.885597image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:09.773775image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:14.610905image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:19.269903image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:23.761221image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:28.379148image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:29.811237image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:31.323528image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:32.724061image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:34.299503image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.740421image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:37.114098image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:38.621626image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:40.001624image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:41.492083image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:43.329172image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:45.351056image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:47.601736image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:50.004793image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:10.063722image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:14.859155image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:19.518375image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:24.024811image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:28.459817image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:29.902461image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:31.401970image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:32.812190image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:34.382449image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.809054image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:37.187006image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:38.697866image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:40.087147image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:41.580669image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:43.424034image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:45.448509image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:47.752874image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:50.079922image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:10.302232image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:15.128846image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:19.784583image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:24.291008image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:28.539702image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:29.973181image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:31.473780image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:32.884602image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:34.463148image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:35.878809image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:37.258051image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:38.768665image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:40.182987image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:41.656276image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:43.526864image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:45.533497image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-10-26T11:16:47.846435image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Correlations

2025-10-26T11:16:55.688389image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
achievementagebmi_rangecore_circledaily_shoutingdaily_stepsdaily_stressdonationflowfruits_veggiesgenderlive_visionlost_vacationpersonal_awardsplaces_visitedsleep_hourssocial_networksufficient_incomesupporting_otherstime_for_passiontodo_completedweekly_meditationwork_life_balance_score
achievement1.0000.0450.0290.298-0.0210.1880.0650.2320.3960.1590.0330.3510.0060.3930.2670.0420.2510.1260.3600.3920.3010.1480.553
age0.0451.0000.2030.0270.0880.0360.0500.1380.0280.1080.0780.0920.0640.0890.0270.0750.0800.1680.1140.0470.0770.0770.081
bmi_range0.0290.2031.0000.0340.0650.1320.0840.0680.0400.0950.0070.0430.0450.0220.1070.1080.0340.0090.0410.0550.0640.0780.249
core_circle0.2980.0270.0341.000-0.0490.1480.0630.2230.2420.1570.1090.240-0.0740.2520.2630.0640.3060.1320.3400.2380.2200.1010.502
daily_shouting-0.0210.0880.065-0.0491.000-0.0270.137-0.037-0.046-0.0650.079-0.0600.100-0.024-0.082-0.0800.0210.086-0.036-0.081-0.127-0.108-0.239
daily_steps0.1880.0360.1320.148-0.0271.0000.0510.1150.1490.2500.0450.130-0.0560.1400.1980.0050.2160.1260.1480.1490.1990.1450.414
daily_stress0.0650.0500.0840.0630.1370.0511.0000.0340.0730.0580.1310.0700.0930.0350.0680.0900.0440.1510.0370.0800.0870.1070.160
donation0.2320.1380.0680.223-0.0370.1150.0341.0000.1720.1990.1310.170-0.0250.2780.209-0.0090.1580.1210.3950.1970.1860.1410.453
flow0.3960.0280.0400.242-0.0460.1490.0730.1721.0000.1360.0270.3300.0150.2270.1560.0300.2480.0790.2840.4900.3000.1330.470
fruits_veggies0.1590.1080.0950.157-0.0650.2500.0580.1990.1361.0000.1120.112-0.0850.1670.2560.1030.1040.1500.2050.1760.2200.1940.442
gender0.0330.0780.0070.1090.0790.0450.1310.1310.0270.1121.0000.0340.0260.0550.0440.0640.0430.0070.1440.0450.0820.0950.055
live_vision0.3510.0920.0430.240-0.0600.1300.0700.1700.3300.1120.0341.000-0.0210.2190.1590.0450.2010.1340.2510.3450.2850.1300.483
lost_vacation0.0060.0640.045-0.0740.100-0.0560.093-0.0250.015-0.0850.026-0.0211.000-0.035-0.138-0.1000.0220.085-0.022-0.022-0.087-0.148-0.264
personal_awards0.3930.0890.0220.252-0.0240.1400.0350.2780.2270.1670.0550.219-0.0351.0000.2700.0090.2090.1540.3340.2470.2420.1470.499
places_visited0.2670.0270.1070.263-0.0820.1980.0680.2090.1560.2560.0440.159-0.1380.2701.0000.1270.1490.1850.2420.1960.2250.2040.528
sleep_hours0.0420.0750.1080.064-0.0800.0050.090-0.0090.0300.1030.0640.045-0.1000.0090.1271.000-0.0410.1240.0000.0710.1150.1560.183
social_network0.2510.0800.0340.3060.0210.2160.0440.1580.2480.1040.0430.2010.0220.2090.149-0.0411.0000.1370.3170.1990.199-0.0100.403
sufficient_income0.1260.1680.0090.1320.0860.1260.1510.1210.0790.1500.0070.1340.0850.1540.1850.1240.1371.0000.1240.0660.2080.0930.410
supporting_others0.3600.1140.0410.340-0.0360.1480.0370.3950.2840.2050.1440.251-0.0220.3340.2420.0000.3170.1241.0000.3350.2520.1410.547
time_for_passion0.3920.0470.0550.238-0.0810.1490.0800.1970.4900.1760.0450.345-0.0220.2470.1960.0710.1990.0660.3351.0000.2800.2020.525
todo_completed0.3010.0770.0640.220-0.1270.1990.0870.1860.3000.2200.0820.285-0.0870.2420.2250.1150.1990.2080.2520.2801.0000.1760.531
weekly_meditation0.1480.0770.0780.101-0.1080.1450.1070.1410.1330.1940.0950.130-0.1480.1470.2040.156-0.0100.0930.1410.2020.1761.0000.400
work_life_balance_score0.5530.0810.2490.502-0.2390.4140.1600.4530.4700.4420.0550.483-0.2640.4990.5280.1830.4030.4100.5470.5250.5310.4001.000

Missing values

2025-10-26T11:16:50.274817image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-26T11:16:50.480658image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timestampfruits_veggiesdaily_stressplaces_visitedcore_circlesupporting_otherssocial_networkachievementdonationbmi_rangetodo_completedflowdaily_stepslive_visionsleep_hourslost_vacationdaily_shoutingsufficient_incomepersonal_awardstime_for_passionweekly_meditationagegenderwork_life_balance_score
07/7/153225052016450755140536 to 50Female609.5
17/7/1523438105225255822232636 to 50Female655.6
27/7/15233441032222458102248336 to 50Female631.6
37/7/15331031072523550575152051 or moreFemale622.7
47/7/1551331042425050700281551 or moreFemale663.9
57/8/1532391010231617108022108351 or moreFemale722.3
67/8/154210610103528875710021081051 or moreMale727.2
77/9/1534535740182810602282221 to 35Female676.2
87/9/1553643354110215100221031021 to 35Female702.8
97/10/15442610100423230600138151 or moreFemale634.4
timestampfruits_veggiesdaily_stressplaces_visitedcore_circlesupporting_otherssocial_networkachievementdonationbmi_rangetodo_completedflowdaily_stepslive_visionsleep_hourslost_vacationdaily_shoutingsufficient_incomepersonal_awardstime_for_passionweekly_meditationagegenderwork_life_balance_score
159623/13/21 11:332074453218210771002621051 or moreMale688.7
159633/13/21 13:2723251086515538755255521 to 35Female685.6
159643/13/21 19:151183101035293337522751051 or moreMale689.0
159653/14/21 1:425424822013373721110221 to 35Female620.1
159663/14/21 3:3944344213266316101268336 to 50Male627.6
159673/14/21 5:42330401004282107701152551 or moreFemale644.5
159683/14/21 6:3033687463175656002105821 to 35Female714.9
159693/14/21 8:3543010108651735270121011021 to 35Male716.6
159703/14/21 8:43111082732161015872216821 to 35Female682.0
159713/14/21 9:03540210105127415852218421 to 35Female651.4

Duplicate rows

Most frequently occurring

timestampfruits_veggiesdaily_stressplaces_visitedcore_circlesupporting_otherssocial_networkachievementdonationbmi_rangetodo_completedflowdaily_stepslive_visionsleep_hourslost_vacationdaily_shoutingsufficient_incomepersonal_awardstime_for_passionweekly_meditationagegenderwork_life_balance_score# duplicates
4910/24/153366736426621884166721 to 35Female641.428
5110/25/153366736426621884166721 to 35Female641.411
2595/12/174210101010952849180221051036 to 50Male744.85
61/15/1603011221260208010210851 or moreFemale575.14
5010/24/172463043313161061072427Less than 20Female631.64
7911/16/163310144201224108082711021 to 35Male660.54
1632/4/16531042101052105101060621031051 or moreFemale731.04
2103/31/1643535435251210703151751 or moreFemale642.94
2234/13/16321064763165718002471021 to 35Male715.54
2464/27/1632561063525471051022431021 to 35Female679.24